Rapid Evolution and the Importance of Recombination to the Gastroenteric
Pathogen Campylobacter jejuni
Daniel J. Wilson,*1 Edith Gabriel, 2 Andrew J.H. Leatherbarrow,à John Cheesbrough,§
Steven Gee,§ Eric Bolton,k Andrew Fox,§k C. Anthony Hart,{3 Peter J. Diggle, and
Paul Fearnhead*
*Department of Maths and Statistics, Lancaster University, Lancaster, United Kingdom; Department of Medicine, Lancaster
University, Lancaster, United Kingdom; àFaculty of Veterinary Science, University of Liverpool, Leahurst, Neston, United Kingdom;
§Preston Microbiology Services, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United
Kingdom; kManchester Medical Microbiology Partnership, P.O. Box 209, Clinical Sciences Building, Manchester Royal Infirmary,
Manchester, United Kingdom; and {Division of Medical Microbiology, School of Infection and Host Defence, University of
Liverpool, Liverpool, United Kingdom
Responsible for the majority of bacterial gastroenteritis in the developed world, Campylobacter jejuni is a pervasive
pathogen of humans and animals, but its evolution is obscure. In this paper, we exploit contemporary genetic diversity
and empirical evidence to piece together the evolutionary history of C. jejuni and quantify its evolutionary potential. Our
combined population genetics–phylogenetics approach reveals a surprising picture. Campylobacter jejuni is a rapidly
evolving species, subject to intense purifying selection that purges 60% of novel variation, but possessing a massive
evolutionary potential. The low mutation rate is offset by a large effective population size so that a mutation at any site
can occur somewhere in the population within the space of a week. Recombination has a fundamental role, generating
diversity at twice the rate of de novo mutation, and facilitating gene flow between C. jejuni and its sister species
Campylobacter coli. We attempt to calibrate the rate of molecular evolution in C. jejuni based solely on within-species
variation. The rates we obtain are up to 1,000 times faster than conventional estimates, placing the C. jejuni–C. coli split
at the time of the Neolithic revolution. We weigh the plausibility of such recent bacterial evolution against alternative
explanations and discuss the evidence required to settle the issue.
Introduction
The World Health Organization expects that every
year 1% of the population of developed nations will suffer
campylobacteriosis (Humphrey et al. 2007), a diarrheal disease that can lead to serious sequelae such as Guillain–
Barré syndrome and reactive arthritis (Zia et al. 2003).
Campylobacter jejuni is the principal bacterial agent responsible for gastroenteritis, ahead of Salmonella, Escherichia
coli, Clostridium, and Listeria combined (Adak et al.
2005). For such a common pathogen, surprisingly little is
known about its evolution. What we do know is that Campylobacter species are zoonotic pathogens that colonize
thegutofawidevarietyofbirdsandmammals.Campylobacter
jejuni, the species responsible for 90% of human disease, is
found commonly in cattle, sheep, pigs, poultry, wild birds,
rabbits, other wild mammals, household pets, molluscs, sewage, and in natural water sources such as rivers and the coast
(Jones 2001; Humphrey et al. 2007). The C. jejuni gene pools
in different host species are largely overlapping (McCarthy
et al. 2007), but population genetic analysis has revealed that
the majority of human cases of disease are caused by isolates
associated with livestock and poultry (Wilson et al. 2008).
Qualitative evidence suggests that adaptation is ongoing in C. jejuni and is facilitated by horizontal gene transfer.
The mechanism of virulence in C. jejuni is still poorly un1
Present address: Department of Human Genetics, University of
Chicago, 920 East 58th Street, CLSC 410, Chicago, IL 60637 USA.
2
Present address: Université d’Avignon, IUT STID, Site Agroparc,
BP 1207, Avignon, France.
3
Deceased.
Key words: Campylobacter jejuni, molecular clock, recombination,
selection, coalescent, Neolithic.
E-mail: [email protected].
Mol. Biol. Evol. 26(2):385–397. 2009
doi:10.1093/molbev/msn264
Advance Access publication November 13, 2008
derstood, but loci responsible for adherence, cellular invasion, toxin production, and flagellar motility are thought to
be important virulence factors (Fouts et al. 2005). The genetic basis for antimicrobial drug resistance is known, and
its spread by recombination has been demonstrated both
within C. jejuni (de Boer et al. 2002) and between related
species (Oyarzabal et al. 2007). The resistance of C. jejuni
to a range of antibiotics is common throughout the world
and is thought to have been driven by frequent use in
animals farmed for meat (Moore et al. 2006).
What we do not know about C. jejuni is how dynamic
is it as a species, what is the timescale of its evolution, how
quickly might it adapt, and what is the extent to which recombination facilitates gene transfer within C. jejuni and to
or from its sister species? Recently, Sheppard et al. (2008)
have suggested that the rate of recombination between
C. jejuni and its sister species Campylobacter coli is sufficient
to have begun to reverse the speciation process, but the
timescale over which that might be happening is unclear.
Integral to these questions is the matter of calibrating
the molecular clock, an issue fraught with difficulty in the
bacterial world. Unlike multicellular eukaryotes, bacteria
do not easily fossilize, and when they do their unremarkable
morphology does not allow accurate taxonomic classification. Unlike viruses, bacteria mutate slowly, so slowly that
the evolution of natural populations has not been readily
measured in real time. To date, the rate of evolution in bacteria
has been calibrated indirectly. Ochman and Wilson (1987)
placed upper and lower bounds on a series of bacterial phylogenetic splits by cross-referencing other events that can be
dated. For example, the common ancestor of mitochondria
and their closest living bacterial relatives must have occurred
more recently than the Palaeoproterozoic increase in atmospheric oxygen and prior to the radiation of mitochondrion-bearing eukaryotes. Moran et al. (1993) calibrated
the molecular clock in bacterial endosymbionts by assuming
Ó 2008 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/
uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
386 Wilson et al.
cospeciation with their aphid hosts, for whom a fossil record
is available. These phylogenetic approaches often conflict
with empirical approaches that are based on laboratory measurements of generation lengths and mutation rates (Lenski
et al. 2003; Ochman 2003).
Our study is based on a longitudinal sample of 1,205 C.
jejuni isolates collected over a 3-year period from patients in
Lancashire, England (Wilson et al. 2008), and DNA sequenced using multilocus sequence typing (MLST, Dingle
et al. 2001). We characterize the ongoing evolution of
C. jejuni using a population genetic (microevolutionary)
model of the forces of drift, mutation, recombination, and
natural selection. We exploit the longitudinal sample to calibrate the molecular clock for C. jejuni directly from withinspecies variation, and we utilize empirical measurements of
generation lengths and mutation rates to quantify the species’
effective population size and evolutionary potential. Finally,
we look at the evolution of C. jejuni in the wider (macroevolutionary) context of the Campylobacter genus, employing
our rate estimates to date phylogenetic splits, such as the
common ancestor of C. jejuni and its sister species C. coli.
We evaluate our rate estimates in light of previous work and
discuss the plausibility of a Neolithic origin of C. jejuni.
Methods
Isolates
We analyzed 1,205 of the C. jejuni isolates of Wilson
et al. (2008) that were available at the time of writing. The
isolates were collected from patients diagnosed with campylobacteriosis and notified through general practitioners and
hospitals to the Preston Microbiology Services Laboratory
in the Preston postcode district between January 1, 2000,
and December 31, 2002. The sampling time corresponds
to the date at which the isolate was received at the laboratory.
The study area covers 968 km2, consisting of both urban
(Preston, Leyland, Chorley, and Garstang) and rural (Ribble
estuary and Ribble valley) districts and comprised 403,000
people at the 2001 census. As is the norm with campylobacteriosis, the cases we studied were sporadic in nature; there
was no evidence for outbreaks. The isolates were sequenced
at seven housekeeping loci (aspA, glnA, gltA, glyA, pgm, tkt,
and uncA) using the MLST (Dingle et al. 2001) producing
3,309 nucleotides in total per isolate.
Analysis Overview
We used a variety of standard tools from a molecular
evolution toolkit in order to infer the evolutionary history of
C. jejuni: Structure (Falush et al. 2003) to identify C. jejuni–
C. coli hybrids, an importance sampler (Fearnhead 2008) to
calibrate the rate of molecular evolution, approximate
Bayesian computation (ABC) (Beaumont et al. 2002) to estimate population genetic parameters for C. jejuni, and
BEAST (Drummond et al. 2002) to reconstruct the phylogeny of the genus Campylobacter.
Identification of C. coli Hybrids
It was necessary to identify C. jejuni–C. coli hybrids
because the microevolutionary models that we subse-
quently used are based on the coalescent (Kingman
1982), which is a probabilistic model for the relatedness
of individuals in a population (the genealogy). Interspecific
gene flow introduces alleles that are very distantly related to
the rest of the population, and therefore violate the assumptions of relatedness made by the coalescent. Gene flow between C. jejuni and its sister species C. coli has been
reported previously (Dingle et al. 2005), so we used Structure (Falush et al. 2003) to identify alleles imported from
C. coli before fitting the evolutionary models.
Informally, Structure assigns individuals to populations (or species in our case) on the basis of allele frequencies. Alleles at one locus that are typically observed to be
associated with C. coli alleles at other loci will be assigned
to C. coli. Although they are sister species, relatively few
alleles are found in both species. Except when there has
been interspecific gene flow, there are usually enough fixed
nucleotide differences between C. jejuni and C. coli alleles
to accurately identify the origin (Dingle et al. 2001). For
each polymorphic nucleotide, Structure gives a posterior
probability of C. jejuni ancestry (as opposed to C. coli ancestry). From this we defined hybrids as isolates with a posterior probability of dual ancestry greater than 0.95. Full
details of the analysis are given in the supplementary
methods, Supplementary Material online.
Microevolution of C. jejuni
The evolutionary models that we employed, both population genetic and phylogenetic, are modular insofar as
they comprise the following components:
Model of mutation
Model of recombination
Model of relatedness (the genealogy or phylogeny)
Method of statistical inference
In evolutionary genetics, the choice of model represents a tradeoff between the competing desires for a biologically realistic model and one for which statistical inference
is feasible. The method of inference is the limiting step:
Simpler models can be fitted using powerful likelihoodbased methods (such as importance sampling [IS] or Markov chain Monte Carlo [MCMC]), which are statistically
efficient in that they exploit all the information the data have
to offer. More complex models can only be fitted using simulation-based methods (such as ABC, Beaumont et al.
2002), which are suboptimal because they use summaries
of the full data.
Recombination in particular massively increases the
complexity of a model, and intraspecific recombination
is frequent in C. jejuni (Fearnhead et al. 2005), both within
and between genes. To learn about the evolution of C. jejuni,
we adopted a two-stage approach that represents a compromise between our competing desires 1) to calibrate the rate of
evolutionary change in C. jejuni and 2) to learn about the
multifarious forces shaping C. jejuni. The former requires
powerful likelihood-based inference to extract what is likely
to be a weak signal, because our sampling period of 3 years
is likely to be short relative to the timescale of bacterial evolution. The latter requires a complex model that incorporates
drift, mutation, selection, and recombination.
Evolution of Campylobacter jejuni 387
0
20 40 60 80
0
20 40 60 80
Nucleotide differences
150 250
0
50
Frequency
100 150
0
50
80
Frequency
120
uncA
pgm
glyA
0
20 40 60 80
Nucleotide differences
0
20 40 60 80
Nucleotide differences
0
40
80 120
tkt
Frequency
40
Frequency
20 40 60 80
Nucleotide differences
50 150 250
gltA
0
0
Nucleotide differences
0
Frequency
100 200 300
0
Frequency
50 100 150
glnA
0
Frequency
aspA
0
20 40 60 80
Nucleotide differences
0
20 40 60 80
Nucleotide differences
FIG. 1.—Histograms of the number of nucleotide differences between each pair of alleles at the seven MLST loci. When Campylobacter
coli–derived alleles are removed, the dark gray portions of the histograms disappear.
In the first step, we fitted the following model that is
simple enough to use a likelihood-based IS method (Example 3 of Fearnhead 2008). The 1,018 isolates were analyzed
for which sampling times were available.
Infinite alleles model of mutation (Kimura and Crow
1964), in which new alleles are generated at rate h. This
rate encompasses the generation of novel alleles by
mutation, intraspecific recombination, and interspecific
recombination.
Free recombination between loci, which is to say the
loci are assumed unlinked or independent.
The coalescent with serial samples (Rodrigo and
Felsenstein 1999), in which the parameter Neg determines the rate of coalescence per year.
Likelihood-based IS (Fearnhead 2008).
The allelic mutation rate h is measured in coalescent
time units of 2Neg years, where Ne is the effective population size and g the generation length in years. In this model,
the alleles identified as C. coli imports were assumed to
have arisen by interspecific recombination, and the dates
of their introduction were estimated. We used priors on
h and Neg that are flat on the logarithmic scale. The object
of inference in this, the first step, was to estimate the timescale of evolution, which is determined by the coalescent
parameter Neg. For an illustration of how Neg affects the
signal of measurable evolution, see supplementary figure
S1, Supplementary Material online.
In the second step, we fitted a more complex model
using ABC (see supplementary methods, Supplementary
Material online, for full details), in which selection is modeled as a form of mutational bias. This analysis was based
on the sequences of 881 pure C. jejuni isolates available at
the time.
Nielsen and Yang (1998) codon model whose parameters are the synonymous mutation rate hS, the
transition–transversion ratio j, and the dN/dS ratio x.
A model of recombination suitable for bacteria (Wiuf
and Hein 2000), whose parameters are the rate of
recombination q and the average length of import s.
The coalescent with serial samples, with parameter Neg.
Simulation-based ABC.
The parameters hS and q are measured per kilobase
(kb), in coalescent time units of 2Neg years. In this model,
C. coli hybrids were excluded from the analysis. Except for
Neg, we used priors that are flat on the logarithmic scale. In
order to calibrate the rate of molecular change, which is to
say convert hS into real-time units of years, we used the
posterior from the simpler model as an informative prior
for Neg. This was necessary because we had found that
ABC was not sufficiently sensitive to estimate Neg.
To distinguish the calibrated parameters from those
measured in coalescent time units, we use lS to denote
the rate of synonymous mutation per kb per year and
r to denote the rate of recombination per kb per year,
whereas hS and q denote the corresponding rates in coalescent time units. Formally, hS 52Ne glS and q52Ne gr. We
also use hN and lN to denote the corresponding rates of nonsynonymous change, and the total mutation rates are
h5hS þ hN and l5lS þ lN .
Macroevolution of Campylobacter
We put the evolution of C. jejuni into a wider context
by inferring the phylogenetic history of seven Campylobacter species for which similar MLST schemes have been designed (Dingle et al. 2001; Miller et al. 2005; van Bergen
et al. 2005). For each species we chose a typical isolate and
tested that there was no interspecies recombination between
the chosen isolates using a permutation test based on the
correlation between physical distance and linkage disequilibrium (LD) (McVean et al. 2002). We then fitted the following phylogenetic model to the concatenated gene
sequences. For further details see supplementary methods,
Supplementary Material online.
Nielsen and Yang (1998) codon model with parameters
lS, j, and x.
No recombination, which is to say the loci are fully
linked.
pgm
6
tkt
3
uncA
17
aspA glnA gltA
33
2
1
glyA
3
pgm
10
tkt
3
uncA
6
pgm
93
tkt
3
uncA
46
ST 1934
0.0 0.5 1.0
ST 1933
aspA glnA gltA glyA
7
4
6
68
aspA glnA gltA
33
39
5
glyA
2
pgm
113
0.0 0.5 1.0
glyA
2
ST 350
ST 1869
aspA glnA gltA glyA
2
1
30
5
tkt
47
uncA
17
pgm
2
tkt
1
uncA
5
tkt
3
uncA
17
ST 2383
0.0 0.5 1.0
aspA glnA gltA
1
4
2
0.0 0.5 1.0
ST 61
0.0 0.5 1.0
0.0 0.5 1.0
388 Wilson et al.
aspA glnA gltA
176
4
2
glyA
2
pgm
6
FIG. 2.—Campylobacter jejuni–Campylobacter coli hybrids identified by Structure. Six sequence types were identified as hybrids. For each hybrid,
the posterior probability of C. jejuni ancestry (as opposed to C. coli ancestry) is shown in gray for the seven MLST loci.
Yule (1924) model, in which speciation occurs at rate k
per 1,000 years.
Likelihood-based MCMC implemented in BEAST
(Drummond et al. 2002).
We used informative priors for j and x, taken from
our posteriors estimated from the microevolutionary model.
On the timescale of the Campylobacter phylogeny, our sequences are essentially sampled contemporaneously.
Therefore, the data do not contain information regarding
the rate of evolution. That was provided entirely by our informative prior on lS, which was taken from the posterior
estimated from the microevolutionary model.
Results
In presenting our results, we begin by scrutinizing genetic diversity and LD in the contemporary population. This
reveals a number of insights, including the identification of
new C. jejuni–C. coli hybrids, and the demonstration that
recombination is the primary mechanism driving molecular
change. Then we proceed to calibrating the real-time rate of
molecular change. We detect a signal of measurable evolution in C. jejuni and employ that calibration to date historical events, including the importation of genes from C. coli
and the most recent common ancestor (MRCA) of C. jejuni.
We discuss the real-time evolutionary potential of C. jejuni,
including the likely efficacy of selection and the size of the
gene pool. Finally, we utilize our estimate of the molecular
clock to calibrate the phylogeny of the genus Campylobacter. We discuss the cultural changes that may have coincided with the C. jejuni–C. coli split and the robustness
of the approach in light of the conflict that arises with
traditional estimates.
Recombination Dominates the Evolution of C. jejuni
A cursory analysis of the patterns of nucleotide diversity in C. jejuni immediately reveals evidence of interspecies gene flow. For each of the seven genes, figure 1
illustrates the number of nucleotide differences between
each pair of alleles. At six loci, there is distinct clustering
between alleles that are genetically similar (in light gray)
and dissimilar (in dark gray). The majority of alleles differ
by fewer than 20 nucleotides, but a small number are highly
divergent, differing by 40 nucleotides or more from the rest.
By comparing our sample of C. jejuni isolates to the C. coli
isolates of Dingle et al. (2005) using the program Structure
(Falush et al. 2003), we found that the genetically divergent
alleles, most of which are observed at low frequency, are
imports from C. coli.
Thirty of 1,205 C. jejuni isolates were found to contain
C. coli alleles. Sequence type (ST-) 61, which carries the
C. coli-derived uncA-17 allele (Dingle et al. 2005), accounted for 23 of those. Five other STs were newly identified as hybrids; for each one, figure 2 illustrates the
posterior probability of C. jejuni ancestry across the seven
loci. ST-1869 and ST-1933, represented by one isolate
each, carried C. coli–derived alleles gltA-30 and aspA33, respectively. ST-2383, also represented by a single isolate, differed from ST-61 by a single nucleotide in aspA.
The only evidence for intragenic mosaicism was found
in pgm-93, carried by ST-350, of which there were two isolates. This allele contains a 200-bp stretch of DNA with
C. coli ancestry, flanked on both sides by sequence of
C. jejuni ancestry. The rarity aspA-33, gltA-30, and pgm93, and the uniformity of the background on which
uncA-17 is found, suggest these genes were introduced singly in four independent ancestral cross-species recombination events. However, the ancestral history of ST-1934,
represented by a single isolate, is likely to be more complex.
Five of the seven genes were C. coli derived, intimating a more
complex history involving multiple cross-species gene transfer events. A single isolate, ST-962, bore C. coli alleles at all
seven genes, suggesting this isolate was misclassified.
Removal of the C. coli alleles erases the genetic discontinuity from patterns of C. jejuni diversity (leaving the
light gray portions of fig. 1) and allows us to identify the
contribution of mutation and selection to population diversity. Pure C. jejuni isolates differed by an average of 49.8
nucleotides out of the 3,309 sequenced by MLST. The majority of differences were synonymous, indicative of the
housekeeping function of the loci. Using ABC, we estimated that a pair of C. jejuni isolates, sampled at the same
time, differ by h 5 13.7 mutations per kb on average, of
which hS 5 11.8 would be synonymous (see table 1 for
credible intervals [CI]). This makes C. jejuni diverse relative to other bacteria (Pérez-Losada et al. 2006). The intense
purifying selection experienced by these genes is reflected
in the small dN/dS ratio of x 5 0.0283. Mutations are also
highly skewed in favor of transitions (j 5 19.0). To emphasize the strength of selection, we used our parameter
Evolution of Campylobacter jejuni 389
Table 1
Estimates of Evolutionary Parameters in Campylobacter Jejuni
A Posteriori
Parameter
Allele model
Mean coalescence time
Neg
h
Allelic mutation rate
1
Sequence model
h
Total mutation rate
Synonymous mutation rate
hS
Nonsynonymous mutation rate
hN
Neutral mutation rate
h0
j
Transition–transversion ratio
x
dN/dS ratio
qs
Recombination rate between distant loci
q
Recombination rate
s
Mean DNA import length
Units
Point Estimate
Years
(2Neg)1
kb1
kb1
kb1
kb1
209
4.38
(2Neg)1
(2Neg)1
(2Neg)1
(2Neg)1
(2Neg)1
kb1 (2Neg)1
kb
13.7
11.8
1.9
35.6
19.0
0.0283
6.08
1.31
4.54
95% CI
155–288
3.75–5.18
8.35–23.9
7.51–19.7
0.84–4.2
22.0–61.8
8.85–39.8
0.0165–0.0492
3.19–11.2
0.0273–42.2
0.100–214
A priori 95% CI
92–820
1.1–9.4
2.1–180
1.9–170
0.2–10
5.8–510
3.3–180
0.0022–0.18
2 104–8 105
0.0014–72
0.015–6,800
The geometric mean was used to obtain point estimates. The (2.5%, 97.5%) quantiles were used to calculate the 95% CI. All priors were uniform on the logarithmic scale.
estimates to calculate that the theoretical mutation rate, in
the absence of selection (i.e., if x 5 1), would be
h0 5 35.6 per kb, some 2.6 times higher than the actual
rate. This implies that purifying selection purges 60% of
novel genetic variation.
Recombination is prevalent in C. jejuni (Fearnhead
et al. 2005), facilitating gene flow within the species, as well
as importing diversity from without. Permutation tests
(McVean et al. 2002) showed significantly lower LD between loci than within P , 0.01), but lacked power to demonstrate significant levels of intragenic recombination on
a locus-by-locus basis. Nevertheless, formal model fitting
revealed nonzero levels of intragenic recombination. Using
ABC, we estimated that an average of q 5 1.31 recombination breakpoints per kb would occur on the evolutionary
branches separating a pair of isolates. Assuming an exponential distribution for the length of DNA imported during
homologous recombination, we estimated an average import length of s 5 4.54 kb. The wide CIs for q and s (table
1) attest to the weak intragenic signal, but these figures are
consistent with those estimated by LDhat (Fearnhead et al.
2005). Appreciably more power was available to estimate
the interlocus rate of recombination. A curious aspect of
bacterial recombination is that residual, nonzero LD is expected even between distant loci (Wiuf and Hein 2000).
This is because the rate of recombination plateaus to a maximum of qs, rather than increasing linearly with physical
distance. We estimated the long-range rate of recombination to be qs 5 6.08, with a relatively tight CI (see table 1).
A simple comparison of the rate of recombination to
mutation conceals the primacy of recombination as the
dominant force that generates molecular change. The higher
rate of point mutation per se is offset by the wider effect that
each recombination event has. Hundreds to thousands of
nucleotides are imported during recombination, of which
a proportion p/1,000 will differ between the incoming
and existing sequence, where p is the average number of
pairwise nucleotide differences per kb in the population.
Therefore, within-species recombination drives molecular
change by a factor qsp=2h more quickly than de novo
mutation, which we estimated to equal 2.67 (95% CI
1.39–4.95).
By quantifying the rate of recombination, we were
able not only to establish the importance of intraspecific recombination relative to mutation but also to compare the
relative importance of intraspecific with interspecific gene
flow. We reasoned earlier that the ancestral history of
C. jejuni bears witness to at least four importations from sister
species C. coli. From our estimate of the within-species recombination rate, we predict that during the same ancestral
history, there were approximately 230 intraspecific recombination events. Although we did not formally infer the rate
of recombination between C. jejuni and C. coli, we can deduce that the rate of cross-species gene flow is little more
than an order of magnitude (roughly 230/4 5 57.5 times)
less frequent than within-species gene flow. This reinforces
the observation that recombination is fundamental, not just
in driving molecular change within C. jejuni but also in
facilitating cross-species gene flow, which is likely to have
important implications for long-term adaptation.
Is Genetic Variation in C. jejuni Just 400 Years Old?
Central to calibrating the rate of molecular evolution is
estimating Neg, the timescale of the genealogy. The product
of the effective population size (Ne) and the generation
length (g), this parameter dictates the rate of coalescence
in C. jejuni, it determines the date of the MRCA, and it allows the rates of mutation and recombination to be measured in real-time units. To have power to estimate Neg,
the population must be measurably evolving on the timescale of the sampling period (Drummond et al. 2003). That
is equivalent to saying there must have been a detectable
number of mutation, recombination, or coalescence events
in the population during the 3-year longitudinal study. Supplementary figure S1, Supplementary Material online, illustrates the idea and shows where in the data the signal lies.
We used the importance sampler of Fearnhead (2008)
to estimate the rate of molecular change in C. jejuni (table
1). To determine whether the population was measurably
evolving, we conducted a formal hypothesis test in which
we compared two models, one using the longitudinal sampling times and the other assuming all sequences were
390 Wilson et al.
Table 2
Dating Ancestral Events
Uncalibrated Date (Units of Neg)
Calibrated Date
Event
TMRCA
TaspA-33
Tpgm-93
TuncA-17
MRCA
Import of aspA-33 from Campylobacter coli
Import of pgm-93 from C. coli
Import of uncA-17 from C. coli
Point Estimate
95% CI
Point Estimate
1,592
August 2000
January 1998
March 1966
1,305–1,772
Jan 1997–Mar 2001
Jul 1965–May 2000
Sep 1726–Jul 1996
1.87
1.34 103
8.73 103
1.20 101
sampled simultaneously. For the two models, measurably
evolving (M1) versus not measurably evolving (M0), we estimated the likelihood averaged over the parameter values
and calculated a Bayes factor, which is the ratio of these
likelihoods. We obtained a Bayes factor of 3.0 1020,
which is much greater than one, indicating very strong support for the measurably evolving hypothesis.
We estimated that Neg 209 years (95% CI 155–288),
which can be understood as the average age of the common
ancestor of a pair of C. jejuni sequences sampled contemporaneously. The magnitude of Neg, together with the fact
that the population is measurably evolving on a timescale of
just 3 years, suggests a rapid rate of evolution in C. jejuni. In
a demographically stable population, the date of the MRCA
is a measure of population turnover and is expected to equal
2Neg. In a recombining population such as C. jejuni, the
MRCA varies between genes and even within a gene.
We estimated a very recent date for the average MRCA
across genes of TMRCA 5 AD 1591 (95% CI 1304–
1771; see table 2). This suggests that the rate of turnover
of genetic variation at the average locus is just 409 years.
The MRCA represents merely the root of the contemporary
genealogy and does not coincide with the birth of the species, but it does mark a horizon beyond which we cannot
use intraspecific genetic variation to reconstruct the evolutionary past. Later, we employ homologous sequences from
other Campylobacter species to infer the deeper history of
C. jejuni.
Evaluating the timescale of evolution in C. jejuni
should allow events other than the MRCA to be dated.
Of particular interest are the dates of the cross-species recombination events identified earlier. Those events are,
namely, the importation of aspA-33, gltA-30, pgm-93,
and uncA-17. We report both calibrated and uncalibrated
dates in table 2; the uncalibrated dates are measured in coalescent time units of Neg. We found that two genes are very
recent imports from C. coli, consistent with their low frequency. The calibrated dates suggest that with 95% probability, aspA-33 was introduced between January 1997 and
March 2001, and pgm-93 was imported between July 1965
95% CI
9.21
8.68
8.25
1.23
101–3.68
105–0.0422
104–0.244
102–1.06
and May 2000. The uncalibrated dates show that these rare
alleles were imported very much more recently than the
TMRCA. We were unable to date the importation of gltA30 because no sampling time was recorded for the isolate
carrying it. However, based on its sample frequency, its importation date is likely to resemble that of aspA-33. The C.
coli-derived uncA-17 is the most frequent and most ancient
import. Present in 25 isolates, we estimated its date of introduction to be March 1966 (95% CI September 1725–July
1995) or 6.5% of the evolutionary time since the MRCA.
Knowledge regarding the timescale of evolution in C.
jejuni allows the absolute rate of molecular change, or the
molecular clock, to be calibrated. The parameter h is the
number of mutations (per kb) by which the average pair
of sequences differs. However, an equivalent interpretation
is that h is the rate of mutation (per kb) in coalescent time
units of 2Neg years. Similarly, q is the rate of recombination
(per kb) per 2Neg years. Therefore, we can combine our
estimate of Neg obtained by IS with our estimates of population genetic parameters obtained by ABC to convert the
mutation and recombination rates from coalescent time to
real time. Table 3 summarizes the results of this conversion.
We inferred an absolute mutation rate of l 5 3.23 102
per kb per year, comprising a synonymous rate of lS 5
2.79 102 and a nonsynonymous rate of lN 5 4.4 103 per kb per year. This corresponds to an average waiting time, per lineage per kb, of 31.0 years before a mutation
arises. We estimated an absolute recombination rate of
r 5 3.07 103 per kb per year, rising to rs 5 1.45 102 per year between distant loci. We go on to use the
absolute rate of synonymous mutation (lS) calculated here
to calibrate the phylogeny of the genus Campylobacter.
We have already seen that C. jejuni experiences intense purifying selection at the housekeeping loci studied
here (x 5 0.0273). In the absence of selection, the absolute
mutation rate would be l0 5 8.39 102 per kb per year
(95% CI 4.85 02 15.1 102), 2.6 times higher than
that observed. Besides reflecting the strict functional constraint of the genes in question, the strength of selection
conveyed by these figures intimates a large effective
Table 3
Calibrated Rate Parameters in Campylobacter jejuni
Parameter
l
lS
lN
l0
rs
r
Units
Total mutation rate
Synonymous mutation rate
Nonsynonymous mutation rate
Neutral mutation rate
Recombination rate between distant loci
Recombination rate
1
Point Estimate
1
kb year
kb1 year1
kb1 year1
kb1 year1
Year1
kb1 year1
3.23
2.79
4.40
8.39
1.45
3.07
2
10
102
103
102
102
103
95% CI
1.86
1.60
2.60
4.85
7.11
4.79
102–5.81
102–5.08
103–7.30
102–1.51
103–2.92
105–1.27
102
102
103
101
102
101
Evolution of Campylobacter jejuni 391
c
d
1.5
0.8
b
0.1
1
10
100
Generation length (hours)
10−7 10−6 10−5 10−4
Mutation rate (per kb)
1.0
Density
0.0
0.5
1.0
Density
0.0
0.0
0.0
0.2
0.5
0.4
Density
1.0
0.5
Density
0.6
1.5
1.5
a
0.1
1
10
10 1000
Effective population size (millions)
0.01 0.1
1
7 30 150
Waiting time (days)
FIG. 3.—Inference based on empirical parameter estimation. Posterior distributions of (a) generation length g, (b) mutation rate m0, (c) effective
population size Ne, and (d) waiting time for a novel mutation W, based on empirical estimates of the generation length (dark gray histograms) or
mutation rate (light gray histograms), and population genetic estimate of h0 (c, d). The empirical data are indicated with black lines crossing the
horizontal axis in (a) and (b). The background histogram in (c) and (d) additionally depends on the population genetic estimate of Neg, rendering them
sensitive to the calibration of the molecular clock. See table 4 for more details.
population size and gene pool. Intense functional constraint
suggests that C. jejuni is already highly adapted, but it is of
interest to quantify the evolutionary potential of the species
or the rate at which it could respond to a change in selection
pressure.
The Evolutionary Potential of C. jejuni Is Immense
The evolutionary potential, or adaptability, of a species
depends on two quantities not readily calculable from population genetic data. The real-time total-population rate of
mutation, Nel0, determines the rate at which advantageous
variants may arise in the event of a change in selection pressure. Together with the amount of standing variation, it determines the rate of adaptation; informally, this is the size of
the gene pool. The effective population size, Ne, limits the
efficacy of selection (Kimura 1955) and hence how likely it
is that an advantageous variant would spread should it arise.
We cannot estimate either quantity directly from the sequence data. However, knowledge of the generation length
(g) or the per-generation neutral mutation rate (m0) would
be sufficient to calculate the quantities of interest, via the
parameters we have been able to infer.
Campylobacter jejuni is a microaerophilic bacterium
that is adapted to growth at 37 or 42 °C, typical of the mammalian and avian gut, respectively. Growth experiments in
culture demonstrate that the generation length depends on
many factors including the genotype, temperature, and the
presence of competing strains (Velayudhan and Kelly 2002;
Khanna et al. 2006; Jackson et al. 2007; Konkel et al. 2007).
An excursion into the recent literature reveals that the doubling time of wild type C. jejuni ranges from 90 min to 5 h,
although we cannot discount longer generation times in
vivo. By assuming that empirical estimates of generation
length follow a log-normal distribution and utilizing vague
priors on the mean and variance of that distribution, we obtained a posterior-predictive distribution on generation
length (see, e.g., Gelman et al. 2003, p. 74). Figure 3a
shows this distribution, with the empirically measured generation times from 10 growth experiments (Velayudhan and
Kelly 2002; Khanna et al. 2006; Jackson et al. 2007; Konkel
et al. 2007) of wild type C. jejuni at 37 and 42 °C, both
alone and in mixed culture, indicated by black lines crossing the horizontal axis. We calculated a point estimate of
2.44 h, with 95% CI of 0.719–8.29 h (table 4).
No direct estimate of the de novo per-generation mutation rate, m0, is available for C. jejuni, but Drake (1991)
and Drake et al. (1998) reported a startlingly consistent pattern in the genomic rate of mutation between different micro-organisms. Per kb, the mutation rates of these
organisms (bacteriophages, E. coli, Saccharomyces cerevisiae, and Neurospora crassa) vary 16,000-fold. Per
genome, the rate varies just 2.5-fold, excepting outliers.
Drake calculated a mean genomic mutation rate of
Table 4
Inference Based on Empirical Parameter Estimates
Parameter
Units
Empirical estimates
g
Generation length
Neutral mutation rate
m0
Composite estimates
Effective population size
Ne
W
Inverse population mutation rate
l0
Neutral mutation rate
Methods denoted by
are calibration sensitive.
Years
Hours
kb1 g1
Days
Days
kb1 year1
Method
Point Estimate
95% CI
Drake’s method
2.79 104
2.44
2.77 106
8.20 105–9.46 104
0.719–8.29
2.26 107–3.41 105
Ne 5 h0/(2m0)
Ne 5 Neg/g W 5 365 (2g)/h0
W 5 365 (4Negm0)/h02
l0 5 m0/g
6.42 106
7.50 105
5.71
0.667
9.95 103
4.95 105–8.29 107
2.13 105–2.64 106
1.52–21.4
0.0437–10.2
6.18 104–1.60 101
In vitro doubling times
392 Wilson et al.
3.3 103, which equals 1.9 106 per kb for C. jejuni,
which has a 1.7-Mb genome (www.nmpdr.org/content/
campy.php). To quantify the uncertainty in this approach,
we again assumed a log-normal distribution for variation in
m0 to obtain a posterior-predictive distribution based on
Drake’s mutation rates, adjusted for genome size. Because
we included outliers, we obtained a point estimate of
2.77 106 per kb and wide CI (table 4). The distribution
is plotted in figure 3b, with the empirically measured mutation rates, adjusted for genome size, indicated by black
lines crossing the horizontal axis.
From these empirical estimates, we can calculate evolutionary quantities of interest. A number of simple formulae relate the parameters g, m0, h0, and Neg to the effective
population size, Ne, and the real-time population mutation
rate, Nel0. An intuitive representation of this latter quantity
is W 5 365 1,000/(Nel0), which gives the expected
waiting time, in days, for a mutation to arise at any particular nucleotide, somewhere in the population. The formulae
for calculating Ne and W are detailed in table 4. When the
formula involves Neg, the estimate is sensitive to the calibration of the molecular clock: Ne is calibration sensitive
when estimated from g but not m0. W is calibration sensitive
when estimated from m0 but not g. By comparing calibration-sensitive and insensitive estimates, this provides a useful check.
The posterior distribution of Ne is plotted as a light
gray histogram in the foreground of figure 3c to emphasize
its dependence on m0 and its insensitivity to calibration. In
the background, in dark gray is plotted the estimate based
on g, which is calibration sensitive. Based on m0, we estimate an effective population size of 6.42 million. The wide
CIs (table 4) principally reflect the underlying uncertainty
in m0. The calibration-sensitive estimate based on g is 0.75
million and has tighter, partially overlapping CIs that reflect
the lesser uncertainty in g. In figure 3d, the estimate of W
based on g (dark gray histogram in foreground) is insensitive to calibration and has tight CIs surrounding the point
estimate of 5.71 days. The estimate based on m0 is 0.667
days (light gray histogram in background); the posterior
distribution has wide CIs and is calibration sensitive.
Qualitatively, the results agree that the effective population size of C. jejuni is large, on the order of hundreds of
thousands to tens of millions. This suggests that selection is
highly efficacious in C. jejuni, sensitive to fitness advantages as small as 1 106. Earlier, we estimated a dN/
dS ratio of 0.0283, suggesting that selection is, for the most
part, purifying. However, a second consequence of the large
effective population size is to counteract the low intrinsic
mutation rate, such that the average waiting time for
a new mutation to arise at any particular nucleotide is just
5.7 days, maybe less. Although most are lost by drift or
purged by selection, novel genetic variants are arising at
such a rate that the potential of C. jejuni to adapt to changes
in selection pressure appears to be immense.
Despite the qualitatively similar conclusions, there exist inconsistencies between the estimates of Ne and W based
on g and m0. These inconsistencies are manifest in the nonoverlapping portions of the light and dark gray histograms
in figure 3c and d. The reason for this partial overlap can be
understood when we use g and m0 to obtain an empirical
calibration of the neutral rate of molecular change in
C. jejuni. Table 4 shows that the empirical point estimate
of l0 is 9.95 103, an order of magnitude lower than that
estimated by our population genetics method from withinspecies variation (table 3). Although the CIs of the empirical estimate are sufficiently wide (table 4) to subsume the
population genetics estimate, the empirical evidence suggests a slower molecular clock overall.
Is Campylobacter Speciating on a Timescale of
Thousands of Years?
Earlier, we remarked that the MRCA of a species constitutes a horizon beyond which we cannot use intraspecific
genetic variation to reconstruct evolutionary history. In a recombining species, the MRCA can differ between loci, but
we estimated that the date of the MRCA for the average
locus in C. jejuni existed around 400 years ago. To delve
deeper into the species’ evolutionary history, it is necessary
to employ more distantly related molecular sequences, so
we used the sequences of six Campylobacter species for
which MLST schemes have also been developed: C. coli
(Dingle et al. 2001), Campylobacter fetus (van Bergen
et al. 2005), Campylobacter helveticus (Miller et al.
2005), Campylobacter insulaenigrae (Stoddard et al.
2007), Campylobacter lari (Miller et al. 2005), and C. upsaliensis (Miller et al. 2005). The MLST schemes for these
species have four genes in common: glnA, glyA, tkt, and
uncA (otherwise known as atpA).
The Campylobacter species we studied exhibit an interesting array of pathogenicities and host ranges. Besides
C. jejuni, which is responsible for 90% of human campylobacteriosis (Gillespie et al. 2002), C. coli, C. lari, and C.
upsaliensis have been documented in sporadic cases or outbreaks of gastroenteritis in humans (Miller et al. 2005).
Campylobacter coli has a host range largely overlapping
with that of C. jejuni albeit with a greater affinity for pigs
(Dingle et al. 2005) and, like its sister species, tends to be
carried asymptomatically in the gut of livestock, poultry,
wild birds, and mammals. Campylobacter lari likewise
has a wide host range but is characterized by its isolation
from seagulls, mussels, and oysters (Miller et al. 2005).
Campylobacter upsaliensis, together with the related C.
helveticus, is associated with domestic cats and dogs (Miller
et al. 2005). Two subspecies are known of C. fetus. C. fetus
subsp. fetus can induce abortion in sheep and less often in
cattle and humans (van Bergen et al. 2005). The genetically
uniform C. fetus subsp. venerealis is cattle restricted, in
which it causes a venereal infection that can lead to infertility and abortion (van Bergen et al. 2005). A somewhat
distinct, reptile-associated strain of C. fetus has also been
described (Tu et al. 2001), which has been documented
in at least one case of human disease (Tu et al. 2004).
The most recently described of these species, C. insulaenigrae, has been isolated from several marine mammals:
common seals and a harbor porpoise in Scotland (Foster
et al. 2004) and northern elephant seals in California
(Stoddard et al. 2007). It too has been observed in a case
of invasive human disease (Chua et al. 2007). Various
Campylobacter species are routinely isolated from sewage
Evolution of Campylobacter jejuni 393
6580
16000
4400
21200
33800
6530
5000 years
(2830 - 8820 years)
FIG. 4.—Phylogeny of the genus Campylobacter. Nodes are labeled with estimated divergence times using BEAST. Error bars associated with
each node indicate relative uncertainty in node height. Uncertainty due to calibration of the molecular clock is represented by a 95% CI below the scale
bar. Posterior uncertainty in the tree topology was negligible. The scale bar was calibrated from intraspecific variation in Campylobacter jejuni. For
alternative scales, see table 7.
and environmental sources, including fresh and marine
water (Jones 2001).
For each species, a typical isolate was chosen (see supplementary methods, Supplementary Material online). As
no recombination was detected between these sequences
(P 5 0.47), we constructed a phylogeny using BEAST
(Drummond et al. 2002), which we calibrated using our
population genetic estimate of the synonymous mutation
rate based on variation within C. jejuni (table 3). The standard method of dating recent bacterial evolution (Achtman
et al. 2004; Roumagnac et al. 2006) is to calibrate the rate of
sequence divergence relative to E. coli and Salmonella typhimurium, which Ochman and Wilson (1987) estimated to
have split 120–160 Ma. They calculated a molecular clock
of 1% per 50 My in the 16S rRNA gene. That would date
the split between C. jejuni and C. coli, which differ by 0.4%
on average (Gorkiewicz et al. 2003), to 10 Ma.
By our method, we obtained a vastly different estimate
of 6,580 years ago (95% CI 3,580–12,400). The phylogeny
in figure 4 is labeled with the inferred split times of all seven
species. Two sources of uncertainty exist in the date estimates. The first source, which is modest in this case, is uncertainty in the split times relative to one another, indicated
by the error bars associated with each node in the phylogeny. The second source, which dominates here, is uncertainty in the calibration of the molecular clock, which
we represent as uncertainty in the scale bar of the phylogeny. Relative and total uncertainty in split times is detailed
in table 5. There was negligible uncertainty in the tree
topology.
Consistent with recent Neighbor-Joining (NJ) phylogenies based on the 16S rRNA, rpoB, and groEL genes
(Kärenlampi et al. 2004; Korczak et al. 2006), we found
that C. jejuni and C. coli are sister species, as are the
pet-associated C. helveticus and C. upsaliensis, and the recently discovered C. insulaenigrae and C. lari. On the
deeper structure, the NJ trees disagreed mutually, and with
our Bayesian phylogeny, except on the observation that
C. fetus is the most divergent of the seven species. The
phylogeny we inferred is consistent with the observation
of Fouts et al. (2005) that C. upsaliensis shares more protein
identity (74.7%) with C. jejuni at the genomic level than
does C. lari (68.9%).
Our method of calibration suggests that Campylobacter is speciating on the order of thousands, rather than millions of years. We dated the root of the tree to 33,800 years
ago (95% CI 19,000–62,600). Using Yule’s model (Yule
1924), a speciation rate of k 5 0.0452 per lineage per
1,000 years was inferred (table 6), which equates to an expected waiting time of 22,000 years between speciation
events.
Calibration of the phylogeny is determined by the mutation rate, which we estimated at l 5 2.93 102 per kb
per year, slightly lower than the mutation rate within C. jejuni. The mutation rate is a function of the synonymous mutation rate (lS), which by our method was estimated from
within C. jejuni, and the transition–transversion ratio (j)
and dN/dS ratio (x). The latter two quantities were coestimated with the phylogeny but informed with prior estimates
from within C. jejuni. As would be expected (Rocha et al.
Table 5
Phylogenetic Split Times in the Genus Campylobacter
95% CI
Split
coli–jejuni
helveticus– upsaliensis
insulaenigrae–lari
helveticus–jejuni
lari–jejuni
fetus–jejuni
Point
Estimate
Relative
Uncertainty
Total
Uncertainty
6,580
4,400
6,530
16,000
21,200
33,800
6,240–6,930
4,190–4,630
6,150–6,940
15,200–16,800
20,300–22,100
32,800–34,800
3,580–12,400
2,400–8,290
3,560–12,500
8,800–30,300
11,600–39,500
19,000–62,600
394 Wilson et al.
Table 6
Evolutionary Parameters in the Genus Campylobacter
Table 7
Alternative Scale Bars for Campylobacter Phylogeny
Parameter
Method
l
j
x
k
Point Estimate
95% CI
2.93 102
1.81
0.0120
0.0452
1.60 102–4.99 102
1.51–2.15
0.00961–0.0152
0.0149–0.120
Intraspecifica
Empiricalb
Ochman–Wilsonc
Coalescentd
Scale (Years)
95% CI
5,000
42,200
7,600,000
23.6Neg
2,830–8,820
2,690–661,000
Not quantified
14.1 Neg–39.6 Neg
a
Based on lS from table 3.
Based on lS calculated from l0 in table 4.
Based on Dingle et al. (2005).
d
Based on hS from table 1.
b
2006), the dN/dS ratio was lower and in this case significantly lower (x 5 0.0120), between Campylobacter species than within C. jejuni, which accounts for the lower
total mutation rate. Curiously, we obtained a transition–
transversion ratio 10-fold lower (j 5 1.81) between Campylobacter species than within C. jejuni.
To compare the intraspecific, population genetics calibration with other methods of calibration, we offer alternative scales in table 7 for the scale bar in figure 4. The
empirical method utilizes the neutral molecular clock estimate from table 4, which is based on in vitro doubling times
and experimental estimates of the per-generation mutation
rate. Using the empirical approach, the length of the scale
bar is 42,200 years with a wide CI (2,690–661,000) that
subsumes the CI for the intraspecific method. The Ochman
and Wilson (1987) method has been used in several recent
publications (e.g., Achtman et al. 2004, Roumagnac et al.
2006) to calibrate the timescale of bacterial evolution and
makes the length of the scale bar 7.6 My. The quantification
of uncertainty in the original paper did not incorporate all
sources of error, and so we do not put forward a CI. Finally,
the coalescent approach uses hS/2 for the synonymous rate
of molecular change, which yields date estimates in coalescent time units of Neg years. The TMRCA for C. jejuni occurred around 2Neg years ago, so this provides a relative
means of dating the phylogeny. The length of the scale
bar is 23.6Neg, with a CI of 14.1–39.6Neg. That makes
the C. jejuni–C. coli split around 17 times more ancient than
the MRCA of C. jejuni.
c
It is possible that epidemiological clustering over time
could cause an artifactual signal of ongoing evolution. For
example, successive epidemics of genetically related
C. jejuni sweeping through the human population could
cause a correlation between sampling time and genetic distance: Organisms sampled closer together in time would be
genetically more similar, mimicking the pattern expected in
a measurably evolving population. There was no obvious
pattern of recurrent epidemics in the data—indeed C. jejuni
generally occurs sporadically (Wilson et al. 2008)—but in
any event it would be difficult to distinguish from ongoing
evolution. In an attempt to eliminate subtle epidemiological
clustering, we thinned the data set so that no two cases occurred fewer than seven days apart, leaving 116 sequences.
Having removed 90% of the sequences, we obtained a much
smaller Bayes factor in favor of the measurably evolving
model of 33.1. We repeated the permutation test as before.
This time, out of 100 permutations, a Bayes factor larger
than 33.1 was obtained three times. This suggests the measurably evolving model is still preferred, but much less so
than before. It remains unclear whether by thinning the
data, a large reduction in the Bayes factor was obtained because epidemiological clustering was causing an artifactual
signal of ongoing evolution or because removing 90% of
the sequences weakens inferential power.
Epidemiological Clustering Might Distort Calibration
In light of the 1,000-fold discrepancy between our intraspecific method of calibrating the rate of Campylobacter
evolution and conventional estimates, we performed further
statistical tests of robustness. We defer biological considerations of the plausibility of our estimates to the discussion. The formal hypothesis test we performed earlier
strongly supported a model in which C. jejuni is measurably
evolving, over one in which it is not. When above 1, the
Bayes factor supports the measurably evolving model,
and when below 1, it supports the simpler model in which
all sequences were treated as if they were sampled simultaneously. We obtained a Bayes factor of 3.0 1020,
which is much greater than 1. However, for completeness,
we performed a permutation test in which the sampling
times of the sequences were randomized 100 times, and
the Bayes factor recomputed in each case in order to obtain
a reference distribution. No Bayes factor even remotely as
large as 3.0 1020 was observed during the permutations,
confirming that a signal consistent with measurable evolution does indeed exist.
Discussion
A Neolithic Origin for C. jejuni
Starkly at variance with conventional estimates of the
timescale of bacteria evolution, how plausible is the molecular clock we estimated from within-species variation? Our
estimate of the C. jejuni–C. coli split time coincides with
the Neolithic domestication of a wide variety of animal
and plant species, a time of broad cultural transition known
as the Neolithic revolution, which began around 10,000
years ago. In particular, a date of 6,580 years ago coincides
roughly with the spread of domestic pigs from the Near East
and the first known domestication of wild boar of European
descent in the Paris Basin, in the early fourth millennium
BC (Larson et al. 2005, 2007). The relevance of this is
the particular association of C. coli with domestic pigs
(Dingle et al. 2005), which invites speculation as to the role
of the advent of the domestic pig in driving the speciation of
C. jejuni or C. coli from their common ancestor.
Interestingly, evidence is accumulating (Mira et al.
2006) that the Neolithic revolution played an important role
Evolution of Campylobacter jejuni 395
in creating new niches for a variety of pathogens of humans
and their domesticated species, perhaps as a result of
changes in agricultural practice or the advent of animal domestication. The genomes of pathogens such as Bordetella
pertussis (whooping cough in humans), Pseudomonas syringae pathovar tomato (bacterial speck on tomatoes), and
Burkholderia mallei (glanders in horses) exhibit a proliferation of insertion sequences connected with niche specialization and subsequent genome reduction/corruption.
However, C. jejuni is unusual in harboring virtually no insertion sequences (Parkhill et al. 2000), and both it and C.
coli retain wide host ranges.
No Resolution for the Bacterial Molecular Clock
Our synonymous rate estimate of lS 5 2.79 102
per kb per year is considerably at odds with traditional estimates of the bacterial molecular clock (Ochman and
Wilson 1987), which would date the C. jejuni–C. coli split
at 10 Ma. There are of course a number of reasons to exercise caution. Our Bayesian approach accounts for all
sources of evolutionary uncertainty under the model, but
uncertainty in the fundamental model assumptions is not
so easily quantified. Extrapolation is the most dangerous
form of statistical prediction, so the assumption that mutation rates are constant within and between species is a major
one. It is widely appreciated that the long-term molecular
clock is slower than short term (Rocha et al. 2006), but this
is generally thought to be accounted for by the delayed effects of purifying selection. Although we attempted to abate
the problem by assuming only that the synonymous rate is
constant, we saw that not just the dN/dS ratio but also the
transition–transversion ratio was significantly lower between species than within C. jejuni. Secondly, we demonstrated that a signal exists indicating that C. jejuni is
measurably evolving on the timescale of our sample, but
we cannot definitively rule out an artifactual association between sampling time and genetic similarity. For example,
concurrent epidemic clusters sweeping through the population might generate such an association, although there was
no evidence for it.
Even so, a rapid bacterial molecular clock cannot be
dismissed out of hand. Indeed, two other attempts to calibrate the rate of bacterial evolution from intraspecific
genetic variation have yielded similar estimates. PérezLosada et al. (2007) estimated a mutation rate of 4.58 102 kb1 year1 for housekeeping genes in Neisseria gonorrhoeae based on surveys of gonorrhoea patients in Baltimore, MD, between 1991 and 2005. Falush et al. (2001)
used sequential biopsies from carriers of Helicobacter pylori in New Orleans, LA, and Colombia to estimate that the
rate of mutation in housekeeping genes may have been as
high as 4.1 102 kb1 year1.
Traditional rate estimates are themselves at odds with
empirical mutation rate estimates (Lenski et al. 2003;
Ochman 2003), as we found here. Per-generation mutation
rates estimated in the laboratory are several orders of magnitude higher than expected from Ochman and Wilson’s
molecular clock, even allowing for natural selection.
Furthermore, certain commensal and pathogenic bacteria
whose divergence can be dated via their host species exhibit
16S rRNA genetic diversity that is higher than expected
from a rate of 1% per 50 My (Mira et al. 2006). Our
own empirical estimates of the rate of molecular evolution
were closer to our population genetic estimates than to the
Ochman–Wilson estimates, differing by a factor of 10 in
their point estimates and overlapping partially in their
CIs. That discrepancy could be explained if Drake’s method
of mutation rate estimation (Drake 1991, Drake et al. 1998)
gives a conservative estimate. Drake’s method makes many
assumptions and does not take account of lineage-specific
knowledge. For example, C. jejuni lacks a number of genes
responsible for DNA repair (Parkhill et al. 2000), which
could underestimate the mutation rate sufficiently to explain the discrepancy between empirical and population genetics estimates. What we can refute is the suggestion (Tu
et al. 2001) that mammal and reptile-associated C. fetus
genotypes diverged 200 Ma, prior to the origin of mammals. This hypothesis is not even supported by the Ochman–Wilson method of calibration, which puts the
C. jejuni–C. fetus split, the root of our phylogeny, at a mere
51.4 Ma.
Conclusion
Patterns of genetic variation within and between species provide an, albeit corrupted, document of evolutionary
history. The picture of evolutionary history painted by genetic diversity in C. jejuni is one of a dynamically evolving
species shaped by frequent recombination and intense purifying selection. Campylobacter jejuni is highly adaptable
by virtue of its large effective population size, which counters a mutation rate that is low in comparison with viral
pathogens (Drake et al. 1998). Together with routine
cross-species gene flow, our analysis reveals a pathogen
with the potential to adapt rapidly to changes in selection
pressure.
Comparison with gene sequences from related species
shows that C. jejuni may have evolved recently, within the
past 12,000 years and possibly in response to changes in
agricultural practice and the advent of animal domestication
coinciding with the Neolithic revolution. Campylobacter is
a dynamic genus in which species are no boundary to gene
flow. Host ranges are wide and overlapping, and zoonosis
between host species is common.
We believe that studies of ancient DNA offer the best
prospect for accurately calibrating recent evolution in bacteria, although progress so far has been limited (Willerslev
et al. 2004; Barnes and Thomas 2006). We have shown that
large within-species samples can be analyzed with complex
evolutionary models that incorporate recombination, without resorting to phylogenetic tree building. By integrating
microevolutionary (population genetic) and macroevolutionary (phylogenetic) approaches in a Bayesian manner,
we were able to quantify cumulative evolutionary uncertainty and perform detailed evolutionary inference. Certain
gaps in our knowledge have been highlighted, including
the generation length of pathogens in vivo and the pergeneration rate of mutation in all but a few species. Finally,
we have proposed that Campylobacter may be evolving on
396 Wilson et al.
a timescale of thousands, rather than millions of years. Our
results contradict the traditional view of the rate of evolution in bacteria, but at the very least, they demand a reevaluation of molecular clock estimates that are widely
in use 20 years on.
Supplementary Material
Supplementary methods and supplementary figure S1
are available at Molecular Biology and Evolution online
(http://www.mbe.oxfordjournals.org/).
Acknowledgments
The authors would like to thank David Balding, Mark
Beaumont, Bob Griffiths, Rosalind Harding, Martin
Maiden, Noel McCarthy, Gil McVean, Julian Parkhill,
Andrew Rambaut, Roisin Ure, and Ian Wilson for advice
and useful discussion. This work is part of the Veterinary
Training Research Initiative, jointly funded by the Higher
Education Funding Council of England and the Department
for Environment, Food, and Rural Affairs. Funding was
also received from the Engineering and Physical Sciences
Research Council.
Literature Cited
Achtman M, Morelli G, Zhu P, et al. (17 co-authors). 2004.
Microevolution and history of the plague bacillus, Yersinia
pestis. Proc Natl Acad Sci USA. 101:17837–17842.
Adak GK, Meakins SM, Yip H, Lopman BA, O’Brien SJ. 2005.
Disease risks from foods, England and Wales, 1996–2000.
Emerg Infect Dis. 11:365–372.
Barnes I, Thomas MG. 2006. Evaluating bacterial pathogen DNA
preservation in museum osteological collections. Proc R Soc
Lond B. 273:645–653.
Beaumont MA, Zhang W, Balding DJ. 2002. Approximate
Bayesian computation in population genetics. Genetics. 162:
2025–2035.
Chua K, Gürtler V, Montgomery J, Fraenkel M, Mayall BC,
Grayson ML. 2007. Campylobacter insulaenigrae causing
septicaemia and enteritis. J Med Microbiol. 56:1565–1567.
de Boer P, Wagenaar JA, Achterberg RP, van Putten JPM,
Schouls LM, Duim B. 2002. Generation of Campylobacter
jejuni genetic diversity in vivo. Mol Microbiol. 44:351–359.
Dingle KE, Colles FM, Falush D, Maiden MCJ. 2005. Sequence
typing and comparison of population biology of Campylobacter coli and Campylobacter jejuni. J Clin Microbiol. 43:
340–347.
Dingle KE, Colles FM, Wareing DRA, Maiden MCJ, Ure MCJ,
Maiden R, Fox AJ, Bolton FE, Bootsma HJ, Willems RJ,
Urwin R, Maiden MC. 2001. Multilocus sequence typing
system for Campylobacter jejuni. J Clin Microbiol. 39:14–23.
Drake JW. 1991. A constant rate of spontaneous mutation in DNAbased microbes. Proc Natl Acad Sci USA. 88:7160–7164.
Drake JW, Charlesworth B, Charlesworth D, Crow JF. 1998.
Rates of spontaneous mutation. Genetics. 148:1667–1686.
Drummond AJ, Nicholls GK, Rodrigo AG, Solomon W. 2002.
Estimating mutation parameters, population history and
genealogy simultaneously from temporally spaced sequence
data. Genetics. 161:1307–1320.
Drummond AJ, Pybus OG, Rambaut A, Forsberg R,
Rodrigo AG. 2003. Measurably evolving populations. Trends
Ecol Evol. 18:481–488.
Falush D, Kraft C, Taylor NS, Correa P, Fox JG, Achtman M,
Suerbaum S. 2001. Recombination and mutation during longterm gastric colonization by Helicobacter pylori: estimates of
clock rates, recombination size, and minimal age. Proc Natl
Acad Sci USA. 98:15056–15061.
Falush D, Stephens M, Pritchard JK. 2003. Inference of population
structure from multilocus genotype data: linked loci and
correlated allele frequencies. Genetics. 164:1567–1587.
Fearnhead P. 2008. Computational methods for complex
stochastic systems: a review of some alternatives to MCMC.
Stat Comput. 18:151–171.
Fearnhead P, Smith NGC, Barrigas M, Fox A, French N. 2005.
Analysis of recombination in Campylobacter jejuni from
MLST population data. J Mol Evol. 61:333–340.
Foster G, Holmes B, Lawsono PA, Thorne P, Byrer DE,
Ross HM, Xerry J, Thompson PM, Collins MD. 2004.
Campylobacter insulaenigrae sp. nov., isolated from marine
mammals. Int J Syst Evol Microbiol. 54:2369–2373.
Fouts DE, Mongodin EF, Mandrell RE, et al. (21 co-authors). 2005.
Major structural differences and novel potential virulence
mechanisms from the genomes of multiple Campylobacter
species. PLoS Biol. 3:e15.
Gelman A, Carlin JB, Stern HS, Rubin DB. 2003. Bayesian data
analysis, 2nd ed. Boca Raton (FL): Chapman Hall.
Gillespie IA, O’Brien SJ, Frost JA, Adak GK, Horby P,
Swan AV, Painter MJ, Neal KR. 2002. A case–case
comparison of Campylobacter coli and Campylobacter jejuni
infection: a tool for generating hypotheses. Emerg Infect Dis.
8:937–942.
Gorkiewicz G, Feierl G, Schober C, Dieber F, Köfer J,
Zechner R, Zechner EL. 2003. Species-specific identification
of Campylobacters by partial 16S rRNA gene sequencing. J
Clin Microbiol. 41:2537–2546.
Humphrey T, O’Brien SJ, Madsen M. 2007. Campylobacters as
zoonotic pathogens: a food production perspective. Int J Food
Microbiol. 117:237–257.
Jackson RJ, Elvers KT, Lee LJ, Gidley MD, Wainwright LM,
Lightfoot J, Park SF, Poole RK. 2007. Oxygen reactivity
of both respiratory oxidases in Campylobacter jejuni: the
cydAB genes encode a cyanide-resistant, low-affinity oxidase
that is not of the cytochrome bd type. J Bacteriol. 189:
1604–1615.
Jones K. 2001. Campylobacters in water, sewage and the
environment. J Appl Microbiol. 90:68S–79S.
Kärenlampi RI, Tolvanen TP, Hänninen M-L. 2004. Phylogenetic
analysis and PCR-restriction fragment length polymorphism
identification of Campylobacter species based on partial groEL
gene sequences. J Clin Microbiol. 42:5731–5738.
Khanna MR, Bhavsar SP, Kapadnis BP. 2006. Effect of
temperature on growth and chemotactic behaviour of
Campylobacter jejuni. Lett Appl Microbiol. 43:84–90.
Kimura M. 1955. Stochastic processes and distributions of gene
frequencies under natural selection. Cold Spring Harbor
Symp. 20:33–53.
Kimura M, Crow JF. 1964. The number of alleles that can be
maintained in a finite population. Genetics. 49:725–738.
Kingman JFC. 1982. On the genealogy of large populations.
J Appl Prob. 19A:27–43.
Konkel ME, Christensen JE, Dhillon AS, Lane AB, HareSanford R, Schaberg DM, Larson CL. 2007. Campylobacter
jejuni strains compete for colonization in broiler chicks. Appl
Environ Microbiol. 73:2297–2305.
Korczak BM, Stieber R, Emler S, et al. 2006. Genetic relatedness
within the genus Campylobacter inferred from rpoB sequences. Int J Syst Evol Microbiol. 56:937–945.
Larson G, Albarella U, Dobney K, et al. (19 co-authors). 2007.
Ancient DNA, pig domestication, and the spread of the
Evolution of Campylobacter jejuni 397
Neolithic into Europe. Proc Natl Acad Sci USA. 104:
15276–15281.
Larson G, Dobney K, Albarella U, et al. (13 co-authors). 2005.
Worldwide phylogeography of wild boar reveals multiple
centers of pig domestication. Science. 307:1618–1621.
Lenski RE, Winkworth CL, Riley MA. 2003. Rates of DNA
sequence evolution in experimental populations of Escherichia coli during 20,000 generations. J Mol Evol. 56:498–508.
McCarthy ND, Colles FM, Dingle KE, Bagnall MC, Manning G,
Maiden MC, Falush D. 2007. Host-associated genetic import
in Campylobacter jejuni. Emerg Infect Dis. 13:267–272.
McVean GAT, Awadalla P, Fearnhead P. 2002. A coalescent
method for detecting recombination from gene sequences.
Genetics. 160:1231–1241.
Miller WG, On SLW, Wang G, Fontanoz S, Lastovica AJ,
Mandrell RE. 2005. Extended multilocus sequence typing
system for Campylobacter coli, C. lari, C. upsaliensis, and C.
helveticus. J Clin Microbiol. 43:2315–2329.
Mira A, Pushker R, Rodrı́guez-Valera F. 2006. The Neolithic
revolution of bacterial genomes. Trends Microbiol. 14:200–206.
Moore JE, Barton MD, Blair IS, et al. (18 co-authors). 2006. The
epidemiology of antibiotic resistance in Campylobacter.
Microbe Infec. 8:1955–1966.
Moran NA, Munson MA, Baumann P, Ishikawa H. 1993. A
molecular clock in endosymbiotic bacteria is calibrated using
insect hosts. Proc R Soc Lond B. 253:167–171.
Nielsen R, Yang Z. 1998. Likelihood models for detecting
positively selected amino acid sites and applications to the
HIV-1 envelope gene. Genetics. 148:929–936.
Ochman H. 2003. Neutral mutations and neutral substitutions in
bacterial genomes. Mol Biol Evol. 20:2091–2096.
Ochman H, Wilson AC. 1987. Evolution in bacteria: evidence for
a universal substitution rate in cellular genomes. J Mol Evol.
26:74–86.
Oyarzabal OA, Rad R, Backert S. 2007. Conjugative transfer of
chromosomally encoded antibiotic resistance from Helicobacter pylori to Campylobacter jejuni. J Clin Microbiol. 45:
402–408.
Parkhill J, Wren BW, Mungall K, et al. (21 co-authors). 2000. The
genome sequence of the food-borne pathogen Campylobacter
jejuni reveals hypervariable sequences. Nature. 403:665–668.
Pérez-Losada M, Browne EB, Madsen A, Wirth T, Viscidi RP,
Crandall KA. 2006. Population genetics of microbial
pathogens estimated from multilocus sequence typing
(MLST) data. Infect Genet Evol. 6:97–112.
Pérez-Losada M, Crandall KA, Zenilman J, Viscidi RP. 2007.
Temporal trends in gonococcal population genetics in a high
prevalence urban community. Infect Genet Evol. 7:271–278.
Rocha EPC, Maynard Smith J, Hurst LD, Holden MTG,
Cooper JE, Smith NH, Feil EJ. 2006. Comparisons of dN/
dS are time dependent for closely related bacterial genomes. J
Theor Biol. 239:226–235.
Rodrigo A, Felsenstein J. 1999. Coalescent approaches to HIV-1
population genetics. In: Crandall K, editor. Molecular
evolution of HIV. Baltimore (MD): John Hopkins University
Press. p. 233–272.
Roumagnac P, Weill F-X, Dolecek C, et al. (11 co-authors).
2006. Evolutionary history of Salmonella typhi. Science. 314:
1301–1304.
Sheppard SK, McCarthy ND, Falush D, Maiden MCJ. 2008.
Convergence of Campylobacter species: implications for
bacterial evolution. Science. 320:237–239.
Stoddard RA, Miller WG, Foley JE, Lawrence J, Gulland FMD,
Conrad PA, Byrne BA. 2007. Campylobacter insulaenigrae
isolates from northern elephant seals (Mirounga angustirostris) in California. Appl Environ Microbiol. 73:1729–1735.
Tu Z-C, Dewhirst FE, Blaser MJ. 2001. Evidence that the
Campylobacter fetus sap locus is an ancient genomic constituent with origins before mammals and reptiles diverged.
Infect Immun. 69:2237–2244.
Tu Z-C, Zeitlin G, Gagner J-P, Keo T, Hanna BA, Blaser MJ.
2004. Campylobacter fetus of reptile origin as a human
pathogen. J Clin Microbiol. 42:4405–4407.
van Bergen MAP, Dingle KE, Maiden MCJ, Newell DG, van der
Graaf-Van Bloois L, van Putten JP, Wagenaar JA. 2005.
Clonal nature of Campylobacter fetus as defined by multilocus sequence typing. J Clin Microbiol. 43:5888–5898.
Velayudhan J, Kelly DJ. 2002. Analysis of gluconeogenic and
anaplerotic enzymes in Campylobacter jejuni: an essential
role for phosphoenolpyruvate carboxykinase. Microbiology.
148:685–694.
Willerslev E, Hansen A, Rønn R, Brand TB, Barnes I, Wiuf C,
Gilichinsky D, Mitchell D, Cooper A. 2004. Long-term
persistence of bacterial DNA. Curr Biol. 14:R9–R10.
Wilson DJ, Gabriel E, Leatherbarrow AJH, Cheesbrough J,
Gee S, Bolton E, Fox A, Hart CA, Diggle PJ, Fearnhead F.
2008. PLoS Genet. 4:e1000203.
Wiuf C, Hein J. 2000. The coalescent with gene conversion.
Genetics. 155:451–462.
Yule GU. 1924. A mathematical theory of evolution, based on
the conclusions of Dr. J.C. Willis. Philos Trans Roy Soc Lond
Ser B. 213:21–87.
Zia S, Wareing D, Sutton C, et al. 2003. Health problems
following Campylobacter jejuni enteritis in a Lancashire
population. Rheumatology. 42:1083–1088.
Rasmus Nielsen, Associate Editor
Accepted November 6, 2008